Fact-checking misinformation on Chinese Social Media: Impact of corrections, awareness prompts, and legal warnings on endorsement

This study investigates the differential impacts of corrections, awareness prompts, and legal warnings on the endorsement of fact-checking information (through both “likes” and expressed support in associated comments) across three types of misinformation motivation (dread, wedge-driving, wish) on Weibo, a major Chinese social media platform. Through manual labeling and BERT (a pretrained large language model), we analyzed a cleaned dataset of 4,942 original fact-checking Weibo posts from 18 November 2010 to 31 May 2022, created or shared by Weibo Piyao. Results indicate that government posts or those with visual cues received fewer “likes” but garnered more supportive comments, while awareness prompts and legal warnings received more supportive comments across three misinformation types. This research provides valuable insights into the practice of fact-checking on social media, highlighting how different strategies may vary in their impact depending on the nature of the misinformation being addressed.

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Publication Details

Journal
New Media & Society
Published
2025-04-30
DOI
https://doi.org/10.1177/14614448251334764
Citations
2
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
8.40

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article

Fact-checking misinformation on Chinese Social Media: Impact of corrections, awareness prompts, and legal warnings on endorsement

Christopher Calabrese, Xiaozhuan Qin, Jiaojiao Ji
2 citations
New Media & Society
Misinformation and Its Impacts
8.40
article

Fact-checking misinformation on Chinese Social Media: Impact of corrections, awareness prompts, and legal warnings on endorsement

Christopher Calabrese, Xiaozhuan Qin, Jiaojiao Ji
article en
2 citations

Abstract

This study investigates the differential impacts of corrections, awareness prompts, and legal warnings on the endorsement of fact-checking information (through both “likes” and expressed support in associated comments) across three types of misinformation motivation (dread, wedge-driving, wish) on Weibo, a major Chinese social media platform. Through manual labeling and BERT (a pretrained large language model), we analyzed a cleaned dataset of 4,942 original fact-checking Weibo posts from 18 November 2010 to 31 May 2022, created or shared by Weibo Piyao. Results indicate that government posts or those with visual cues received fewer “likes” but garnered more supportive comments, while awareness prompts and legal warnings received more supportive comments across three misinformation types. This research provides valuable insights into the practice of fact-checking on social media, highlighting how different strategies may vary in their impact depending on the nature of the misinformation being addressed.

New Media & Society
University of Science and Technology of China (CN), Clemson University (US)
Chinese Academy of Sciences
Openalex Percentile: Top 3%
Misinformation and Its Impacts
8.40
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